This thesis investigates how the assessment of circular economy (CE) at the macro-economic level can be facilitated and promoted. First, a study on the socio-economic environmental impacts of... Show moreThis thesis investigates how the assessment of circular economy (CE) at the macro-economic level can be facilitated and promoted. First, a study on the socio-economic environmental impacts of international agricultural supply chain is presented to better exemplify how Multi-Regional Environmental Extended Input-Output (MR EEIO) data can be used to support policy making. Then, a Python software package (pycirk) and methods for standardized and replicable CE scenarios are presented with a case study on the global environmental and socio-economic impacts CE strategies. The thesis also presents an easy to use and open-source web-based tool for CE scenario construction and analysis (RaMa-Scene). Through these studies, MR EEIO appears to be an adequate tool to assess CE scenarios. However, the implementation of CE interventions will require a variety of micro-level changes across the current international production and consumption system and in many cases more detailed data is required than what is currently available in existing MR EEIO databases. Data availability for CE assessment could be increased through the use of Computer-Aided Technologies and Artificial Intelligence methods in combination with Life Cycle Inventory modelling and MR EEIO databases, but this is only one potential way forward. In fact, the industrial ecology and circular economy communities have many opportunities ahead to improve data collection practices by leveraging digital technologies and artificial intelligence methods. However, coordination in these scientific communities is needed to ensure that the full potential of these technological developments is harvested for the benefit of a sustainable circular economy and society. Show less
A fuller understanding of drug-related violence requires good quality data. Having such data consistently up-to-date will provide benefit in policy-making and evaluation, as well as for operational... Show moreA fuller understanding of drug-related violence requires good quality data. Having such data consistently up-to-date will provide benefit in policy-making and evaluation, as well as for operational, monitoring and research purposes. For policy-makers, accurate data on drug-related violence will provide a fuller picture of the drugs trade and its societal impact — essential for planning and assessing policy responses, priority setting and resource allocation. Show less
According to the minimum description length (MDL) principle, data compression should be taken as the main goal of statistical inference. This stands in sharp contrast to making assumptions about an... Show moreAccording to the minimum description length (MDL) principle, data compression should be taken as the main goal of statistical inference. This stands in sharp contrast to making assumptions about an underlying ``true'' distribution generating the data, as is standard in the traditional frequentist approach to statistics. If the MDL premise of making data compression a fundamental notion can hold its ground, it promises a robust kind of statistics, which does not break down when standard, but hard to verify, assumptions are not completely satisfied. This makes it worthwhile to put data compression to the test, and see whether it really makes sense as a foundation for statistics. A natural starting point are cases where standard MDL methods show suboptimal performance in a traditional frequentist analysis. This thesis analyses two such cases. In the first case it is found that although the standard MDL method fails, data compression still makes sense and actually leads to the solution of the problem. In the second case we discuss a modification of the standard MDL estimator that has been proposed in the literature, which goes against its data compression principles. We also review the basic properties of R_nyi's dissimilarity measure for probability distributions. Show less
A data mining scenario is a logical sequence of steps to infer patterns from data. In this thesis, we present two scenarios. Our first scenario aims to identify homogeneous subtypes in data. It was... Show moreA data mining scenario is a logical sequence of steps to infer patterns from data. In this thesis, we present two scenarios. Our first scenario aims to identify homogeneous subtypes in data. It was applied to clinical research on Osteoarthritis (OA) and Parkinson’s disease (PD) and in drug discovery. Thus, because OA and PD are characterized by clinical heterogeneity, a more sensitive classification of the cohort of patients may contribute to the search for the underlying diseases mechanism. In drug discovery, subtyping may improve the understanding of the similarity (and distance) between different phenotypic effects as induced by drugs and chemicals. Our second scenario aims to compare text classification algorithms. First, we show that common classifiers achieve comparable performance on most problems. Second, tightly constrained SVM solutions are high performers. In that situation, most training documents are bounded support vectors, SVM reduces to a nearest mean classifier and no training is necessary, which raises a question on SVM merits in sparse bag of words feature spaces. Also, SVM is shown to suffer from performance deterioration for particular combinations of training set size/number of features. This relate to outlying documents of distinct classes overlapping in the feature space. Show less
